Salmon subsidies alleviate nutrient limitation of benthic biofilms in southeast Alaska streams
Bibliographic record
Abstract
Using nutrient-diffusing substrata (NDS) in seven streams in southeast Alaska, USA, we tested whether (i) nutrient limitation of autotrophic and heterotrophic biofilms was alleviated by salmon resource subsidies, and (ii) whether the degree of alleviation could be predicted by environmental variables. Before salmon spawners arrived, autotrophic biofilms were nitrogen (N)-limited, or co-limited by N and phosphorus (P), whereas heterotrophic biofilms were either P-limited, or co-limited by N and P. Combined N and P amendments resulted in a 2.6-fold increase in biofilm chlorophyll a, and a 3.2-fold increase in community respiration. After salmon arrived, autotroph nutrient limitation was alleviated in six of the seven streams. Heterotrophs still exhibited nutrient limitation in six streams, but most streams shifted from co-limitation to P-limitation. Nutrient-diffusing substrata amended with salmon tissue indicated that salmon could also be an important source of organic carbon for biofilms. Autotrophs responded less to N and P amendments as streamwater ammonium concentration increased with the arrival of salmon. For heterotrophs, ammonium concentration and N:P ratio best predicted changes in response following the arrival of salmon. We provide the first direct evidence that biofilm nutrient limitation can be alleviated by salmon spawners in nutrient-poor streams.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".